Deep Learning For Hydrometeorology And Environmental Science
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Deep Learning for Hydrometeorology and Environmental Science
This book provides a step-by-step methodology and derivation of deep learning algorithms as Long Short-Term Memory (LSTM) and Convolution Neural Network (CNN), especially for estimating parameters, with back-propagation as well as examples with real datasets of hydrometeorology (e.g. streamflow and temperature) and environmental science (e.g. water quality). Deep learning is known as part of machine learning methodology based on the artificial neural network. Increasing data availability and computing power enhance applications of deep learning to hydrometeorological and environmental fields. However, books that specifically focus on applications to these fields are limited. Most of deep learning books demonstrate theoretical backgrounds and mathematics. However, examples with real data and step-by-step explanations to understand the algorithms in hydrometeorology and environmental science are very rare. This book focuses on the explanation of deep learning techniques and their applications to hydrometeorological and environmental studies with real hydrological and environmental data. This book covers the major deep learning algorithms as Long Short-Term Memory (LSTM) and Convolution Neural Network (CNN) as well as the conventional artificial neural network model.
Modeling and Monitoring Extreme Hydrometeorological Events
In a world experiencing increasingly intense hydrometeorological events driven by climate change, the need for effective solutions is paramount. Modeling and Monitoring Extreme Hydrometeorological Events presents a cutting-edge exploration of the challenges posed by flash droughts and floods, offering innovative methodologies and tools to address these global issues. Through a combination of computer modeling, remote sensing, artificial intelligence, and case studies, this book provides a comprehensive framework for understanding and mitigating the impacts of extreme hydrometeorological events. It examines the rapid emergence of flash droughts, which bring devastating consequences to agriculture, water resources, ecosystems, and public health. The book also delves into the complex dynamics of flash floods, exploring their causes, impacts, and potential solutions. With a focus on water management, the book addresses knowledge gaps, provides adaptation and mitigation strategies, and emphasizes the importance of climate change considerations. It aims to empower scientists, policymakers, professionals, and educators to develop effective policies and decision-making frameworks to combat the increasing risks posed by extreme hydrometeorological events. Written by a diverse team of experts in hydrology, hydrometeorology, emergency management, civil engineering, and related fields, this book offers valuable insights and practical tools for researchers, professors, graduate students, policymakers, and professionals.
Deep Learning for the Earth Sciences
Author: Gustau Camps-Valls
language: en
Publisher: John Wiley & Sons
Release Date: 2021-08-16
DEEP LEARNING FOR THE EARTH SCIENCES Explore this insightful treatment of deep learning in the field of earth sciences, from four leading voices Deep learning is a fundamental technique in modern Artificial Intelligence and is being applied to disciplines across the scientific spectrum; earth science is no exception. Yet, the link between deep learning and Earth sciences has only recently entered academic curricula and thus has not yet proliferated. Deep Learning for the Earth Sciences delivers a unique perspective and treatment of the concepts, skills, and practices necessary to quickly become familiar with the application of deep learning techniques to the Earth sciences. The book prepares readers to be ready to use the technologies and principles described in their own research. The distinguished editors have also included resources that explain and provide new ideas and recommendations for new research especially useful to those involved in advanced research education or those seeking PhD thesis orientations. Readers will also benefit from the inclusion of: An introduction to deep learning for classification purposes, including advances in image segmentation and encoding priors, anomaly detection and target detection, and domain adaptation An exploration of learning representations and unsupervised deep learning, including deep learning image fusion, image retrieval, and matching and co-registration Practical discussions of regression, fitting, parameter retrieval, forecasting and interpolation An examination of physics-aware deep learning models, including emulation of complex codes and model parametrizations Perfect for PhD students and researchers in the fields of geosciences, image processing, remote sensing, electrical engineering and computer science, and machine learning, Deep Learning for the Earth Sciences will also earn a place in the libraries of machine learning and pattern recognition researchers, engineers, and scientists.